Quadrotor Unmanned Aerial Vehicle Research Articles

Fixed-Time Controller for Altitude/Yaw Control of Mini-Drones: Real-Time Implementation with Uncertainties

Gradually, it has become easier to use aerial transportation systems in practical applications. However, due to the fixed-length wire, recent studies on load-suspended transportation systems have revealed some practical constraints, especially when using quadrotor unmanned aerial vehicles (UAVs). By actively adjusting the distance between the quadrotor and the payload, it becomes possible to carry out a variety of challenging tasks, including traversing confined spaces, collecting samples from offshore locations, and even landing a payload on a movable platform. Thus, mass variable aerial transportation systems should be equipped with trajectory tracking control mechanisms to accomplish these tasks. Due to the above-mentioned reasons, the present paper addresses the problem of the altitude/yaw tracking control of a mini-quadrotor subject to mass uncertainties. The main objective of this paper is to design a fixed-time stable controller for the perturbed altitude/yaw motions, based on recent results using the fixed-time stability approach. For comparison reasons, other quadrotor motion controllers such as dual proportional integral derivative (PID) loops were considered. To show its effectiveness, the proposed fixed-time controller was validated on a real mini-quadrotor under different scenarios and has shown good performance in terms of stability and trajectory tracking.

Finite-time integral fast terminal sliding mode control for uncertain quadrotor UAV based on state-dependent Riccati equation observer subjected to disturbances

This paper presents a new control approach of trajectory tracking for quadrotor unmanned aerial vehicle (UAV) systems under external disturbances and parameter uncertainties. The integral fast terminal sliding mode control (IFTSMC) approach applying state-dependent Riccati equation (SDRE) observer is presented for this purpose. As a result, the nonlinear SDRE observer is designed to estimate the non-measurable states. Afterward, employing the estimated states of the SDRE observer, the proposed IFTSMC controller is designed for trajectory tracking of the perturbed quadrotor UAV. Besides, applying Lyapunov’s stability theory, the finite-time convergence and robustness of the closed-loop UAV system are assured. Based on the numerical simulation results, the proposed controller successfully tracked various reference trajectories of quadrotor UAVs despite external disturbances and parametric uncertainty.

Barrier Lyapunov function and adaptive backstepping-based control of a quadrotor UAV

AbstractThis paper presents backstepping control and backstepping constraint control approaches for a quadrotor unmanned aerial vehicle (UAV) control system. The proposed methods are applied to a Parrot Mambo drone model to control rotational motion along the $x$ , $y$ , and $z$ axes during hovering and trajectory tracking. In the backstepping control approach, each state of the system controls the previous state and is called “virtual control.” The last state is controlled by the real control input. The idea is to compute, in several steps, a control law that ensures the asymptotic stability of the system. The backstepping constraint control method, based on barrier Lyapunov functions (BLFs), is designed not only to track the desired trajectory but also to guarantee no violation of the position and angle constraints. Symmetric BLFs are introduced in the design of the controller. A nonlinear mathematical model is considered in this study. Based on Lyapunov stability theory, it can be concluded that the proposed controllers can guarantee the stability of the UAV system and the state converges asymptotically to the desired trajectory. To make the control robust, an adaptation law is applied to the backstepping control that estimates the unknown parameters and ensures their convergence to their respective values. Validation of the proposed controllers was performed by simulation on a flying UAV system.

NN-Based Parallel Model Predictive Control for a Quadrotor UAV

A novel neural network (NN)-based parallel model predictive control (PMPC) method is proposed to deal with the tracking problem of the quadrotor unmanned aerial vehicles (Q-UAVs) system in this article. It is well known that the dynamics of Q-UAVs are changeable while the system is operating in some specific environments. In this case, traditional NN-based MPC methods are not applicable because their model networks are pre-trained and kept constant throughout the process. To solve this problem, we propose the PMPC algorithm, which introduces parallel control structure and experience pool replay technology into the MPC method. In this algorithm, an NN-based artificial system runs in parallel with the UAV system to reconstruct its dynamics model. Furthermore, the experience replay technology is used to maintain the accuracy of the reconstructed model, so as to ensure the effectiveness of the model prediction algorithm. Furthermore, a convergence proof of the artificial system is also given in this paper. Finally, numerical results and analysis are given to demonstrate the effectiveness of the PMPC algorithm.

Multi-volume variable scale bitmap data object classification algorithm architectural concrete color difference detection

Architectural concrete color difference (ACCD) directly affects building aesthetics. At present, the AC apparent quality detection method is based on a subjective artificial observation and detection process. The large volume of AC building, however, makes it difficult for quality inspectors to sample on site. Therefore, the manuscript establishes the aerial vehicle-to-ground coordinate conversion model and quadrotor unmanned aerial vehicle (QUAV) Eulerian motion model wherein the real-time optimization of QUAV motion parameters through remote sensing technology improves data collection stability. The proposed ACCD detection model, which is based on the QUAV remote sensing technology, is established by the improved multi-volume bitmap data object classification algorithm (BDOCA). The detection accuracy of MobileNetV3, ResNet50, and VGG16 algorithms reach 86.33%, 92.10%, and 87.62%, respectively. The study acquires experimental images from different buildings and produces a generalized identification set. It then verifies the ACCD detection model generalized identification capability by the traversal type detection method. The MobileNetV3, ResNet50, and VGG16 algorithms all achieved a generalized identification accuracy of 99.6%. The study builds the ACCD model by red-green-blue (RGB) spatial conversion, Kalman filtering noise reduction, noise point open operation, connected domain close operation, and mask quantization segmentation operation. This method implements color difference block coding, color difference threshold pixel point scale calculation, and geometric parameter calculation.

Distributed Adaptive Human-in-the-Loop Event-Triggered Formation Control for QUAVs With Quantized Communication

To improve the safety and reliability of quadrotor unmanned aerial vehicles (QUAVs) with limited communication, system uncertainties, and unknown external disturbances in a highly uncertain and safety-critical environment, a distributed adaptive human-in-the-loop (HiTL) event triggered (ET) formation controller is proposed. The non-autonomous leader are controlled by receiving control commands decided by the gesture recognition system, and the followers are controlled indirectly via the connected communication network. Thus, the safety and flexibility of the closed-loop system are improved. The designed controller is quantized and then sent to actuator only at the ET instants to further reduce the network burden. The radial basis function neural network (RBFNN) is used to approximate the system uncertainties. The unknown approximation error and the unknown external disturbance are viewed as a compound disturbance compensated by the high order disturbance observer (HODO). In addition, the uniformly ultimately bounded (UUB) stability of the closed-loop system is achieved through the Lyapunov method. Finally, comparative experiments are implemented on the QUAVs to demonstrate the validity of the presented control scheme.

Discrete-Time Adaptive Neural Tracking Control and Its Experiments for Quadrotor Unmanned Aerial Vehicle Systems

For the control of a quadrotor unmanned aerial vehicle, the strong nonlinearities, coupling, and underactuated problem in both positioning and attitude systems of the quadrotor are major challenging issues to be solved. In this article, a discrete-time adaptive dynamic surface control (DSC) scheme for the quadrotor is proposed to obtain a satisfactory tracking performance. The nonlinearities and couplings are overcome by employing the designed robust adaptive DSC nonlinear control method. The underactuated problem is overcome by solving the designed adaptive neural control equations. Also, different from the continuous-time control scheme, the discrete-time control is more suitable for the computer and network control in practicable cases. Furthermore, the digital first-order low-pass filters are constructed to predict the future virtual control signal in the backstepping method, leading to the avoidance of the model transformation problem in the discrete-time backstepping method.

Fixed-Time Disturbance Observer-Based Distributed Formation Control for Multiple QUAVs

In this article, the problem of fixed-time distributed formation control for multiple quadrotor unmanned aerial vehicles (QUAVs) suffering from external disturbances is investigated. Resorting to the fixed-time disturbance observer (FTDO), the unknown disturbances are accurately estimated without requiring the information of initial observer errors. Based on the fusion of command filter technique, nonsmooth error compensation mechanism and backstepping design method, the proposed singularity-free distributed formation controllers ensure that the whole closed-loop system is practical fixed-time stable, and the formation tracking errors converge to a small neighbourhood of the zero in a fixed time. Finally, a numerical simulation reveals the validity of the devised distributed formation control scheme.

Adversarial Multiagent Output Containment Graphical Game With Local and Global Objectives for UAVs

Multiple leader and follower graphical games constitute challenging problems for aerospace and robotics applications. One of the challenges is to address the mutual interests among the followers with an optimal control point of view. In particular, the traditional approaches treat the output containment problem by introducing selfish followers where each follower only considers their own utility. In this article, we propose a differential output containment game over directed graphs where the mutual interests among the followers are addressed with an objective functional that also considers the neighboring agents. The obtained output containment error system results in a formulation where outputs of all followers prove to converge to the convex hull spanned by the outputs of leaders in a game optimal manner. The output containment problem is solved using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathscr{H}_\infty$</tex-math></inline-formula> output feedback method, where the new necessary and sufficient conditions are presented. Another challenge is to design distributed Nash equilibrium control strategies for such games, which cannot be achieved with the traditional quadratic cost functional formulation. Therefore, a modified cost functional that provides both Nash and distributed control strategies in the sense that each follower uses the state information of its own and neighbors is presented. Furthermore, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathscr{L}_{2}$</tex-math></inline-formula> gain bound of the output containment error system that experiences worst-case disturbances with respect to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathscr{H}_\infty$</tex-math></inline-formula> criterion is investigated. The proposed methods are validated by means of multiagent quadrotor unmanned aerial vehicles output containment game simulations.

Unknown Input Observer-Based Fixed-Time Trajectory Tracking Control for QUAV with Actuator Saturation and Faults

The trajectory tracking control problem of a quadrotor unmanned aerial vehicle (QUAV) subject to external disturbances, inertia uncertainties, actuator faults, and input saturation is addressed in this paper. In contrast with previous works, input saturation herein refers to rotor speed saturation rather than thrust and torque saturation. First, the control system is decoupled into translational and rotational subsystems. Then, for both subsystems, two novel fixed-time unknown input observers (UIO) based on disturbance filtering are developed to estimate the lumped disturbance rapidly and precisely without awareness of the boundary of disturbances. Furthermore, fixed-time tracking controllers for translational and rotational subsystems are proposed based on the estimation values provided by UIO to stabilize tracking errors into a small region in fixed time regardless of the initial values. The theoretical analysis based on the Lyapunov method is presented to demonstrate the stability. Finally, the simulation results show that the proposed control method is effective. The comparison simulation is carried out to validate superiority of the proposed observer and its advantage can be summed up as: (1) the upper bound of the disturbance or its derivative is not needed; (2) the estimation results are smoother and the observation precision is higher due to the absence of sign function; (3) the mutant disturbance can be also estimated quickly and precisely.

Active fault-tolerant control of quadrotor UAVs with nonlinear observer-based sliding mode control validated through hardware in the loop experiments

Multirotor unmanned aerial vehicles (UAV) are highly prone to motor faults, which can arise from defective motors or damaged propellers. Motor faults severely change the multirotor UAV’s dynamics and therefore endanger flight safety and reliability since the controller loses its efficiency. To cope with such crucial problems, a fault-tolerant controller is proposed in this paper for full control of a quadrotor UAV with motor faults. The proposed fault-tolerant approach consists of the nonlinear observer technique and the Sliding Mode Control (SMC). The designed novel nonlinear observer predicts the effects of motor faults on quadrotor dynamics and it is augmented with an SMC to create a fault-tolerant controller. In addition, the nonlinear observer enhances the robustness of the SMC against the uncertainties and disturbances acting on the quadrotor during flight. Any actuator fault will be treated as a disturbance detected by the nonlinear observer and will be attenuated directly by the proposed SMC. The disturbance attenuation capability achieved by the nonlinear observer decreases the amount of control action expected from the SMC, which results in advanced robustness without sacrificing the nominal control performance. The performance of the proposed nonlinear observer SMC (NOSMC) is demonstrated through simulations and testbed experiments. The results show that the proposed fault-tolerant controller effectively recovers the full control of the quadrotor with motor faults up to 40% while tracking the predefined trajectory and rotation angles as desired.

Control of UAV quadrotor using reinforcement learning and robust controller

AbstractThe control of unmanned aerial vehicle quadrotor is challenging because of non‐linearities, coupling and disturbance. Here, a novel control method which includes reinforcement learning (RL) component and robust component is proposed. In this method, the RL component only relies on collected data instead of modelling to handle coupling and disturbance from aerodynamics and model. To ensure safety during training and improve training speed, the robust component is used to reduce the disturbance .The stability of the system with our controller is proven by Lyapunov method. The results of the simulation exhibit advanced performance of our controller.

Image-based prescribed performance visual servoing control of a QUAV with hysteresis quantised input

ABSTRACT In this paper, a novel image-based prescribed performance visual servoing control scheme with hysteresis quantised input is proposed for quadrotor unmanned aerial vehicles (QUAVs, for short). First, based on the perspective projection principle, effective image features are defined on a image plane called virtual image plane, and the decoupled image feature dynamics are achieved with respect to target points. Then, the image feature dynamics and QUAV dynamics are combined to derive a nonlinear dynamic system, and the nonlinear system is decoupled into two subsystems based on the commonly used inner–outer loop control framework. Furthermore, the prescribed performance control-based visual servoing control scheme with hysteresis quantizer is designed, and the necessary stability proof is presented. Finally, the numerical simulations and experiments are conducted to validate the effectiveness of the proposed control scheme.

The usefulness of sensor fusion for unmanned aerial vehicle indoor positioning

PurposeIn this study, an indoor sensor information fusion positioning system of the quadrotor unmanned aerial vehicle (UAV) was investigated to solve the problem of unstable indoor flight positioning.Design/methodology/approachThe presented system was built on Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU) and LiDAR-Lite devices. Based on this, one can obtain the aircraft's current attitude and the position vector relative to the target and control the attitudes and positions of the UAV to reach the specified target positions. While building a UAV positioning model relative to the target for indoor positioning scenarios under limited Global Navigation Satellite Systems (GNSS), the system detects the environment through the NVIDIA Jetson TX2 (Transmit Data) peripheral sensor, obtains the current attitude and the position vector of the UAV, packs the data in the format and delivers it to the flight controller. Then the flight controller controls the UAV by calculating the posture to reach the specified target position.FindingsThe authors used two systems in the experiment. The first is the proposed UAV, and the other is the Vicon system, our reference system for comparison purposes. Vicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.Originality/valueVicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.

A hybrid thrusting system for increasing the endurance time of multirotor unmanned aerial vehicles

One of the most significant disadvantages of electric multirotor unmanned aerial vehicles is their short flight time compared to fuel-powered unmanned aerial vehicles. This is mainly due to the low energy density of electric batteries. Fuel has much more energy density when compared to batteries. Electric-powered motors in multirotor unmanned aerial vehicles cannot be replaced with fuel-based engines because the stability and control of multirotor unmanned aerial vehicles rely on the high response rates of electric motors. One of the possible solutions to overcome this problem of short endurance times is by using hybrid thrusting systems that combine the advantages of both fuel and electrical propulsion systems, where high maneuverability and long endurance flight time could be achieved. In this work, hybrid thrusting and power systems for multirotor unmanned aerial vehicles are studied. Targeted hybrid thrusting systems consist of combustion engines, electric motors, and their power sources. Then a hybrid thrusting system-based quadrotor unmanned aerial vehicle model is developed. The article presents the altitude and attitude control systems of the developed hybrid thrusting system-based unmanned aerial vehicle. The presented hybrid quadcopter model comprises four electric motors and one fuel engine. The fuel engine used in this work is a 4.07 cc internal combustion engine targeting 2–3 kg unmanned aerial vehicles with up to 5 kg maximum takeoff weight. The developed hybrid quadrotor unmanned aerial vehicle achieved a 139% improvement in flight time when compared with traditional electric-based quadrotor unmanned aerial vehicles. The article also reports on other flight time-related issues such as the optimal fuel mass to battery size ratio to maximize the endurance time of the quadrotor unmanned aerial vehicles.

Structural fault diagnosis of UAV based on convolutional neural network and data processing technology

ABSTRACT This study presents a novel method for damage detection and identification in unmanned aerial vehicles (UAVs) using vibration data gathering and processing technologies based on deep learning. To conduct the study, a quad-rotor UAV was manufactured, and a vibration data acquisition system was developed to collect vibration data along three axes under normal and three damage scenarios. Empirical mode decomposition (EMD) was employed to reduce high-frequency noise in the signals, and the root mean square error (RMSE) feature was utilised to select the Y-axis acceleration data, which exhibits significant changes across different damage cases. Finally, a convolutional neural network was used to identify the damage based on the vibration data. Experimental results demonstrate that the proposed method achieved 97.5% accuracy using selected and noise-reduced Y-axis acceleration data, thereby indicating its usefulness in diagnosing damage types in multi-rotor UAVs.

Design of a Closed-Loop Error-in-Variable System Controller and Its Application in Quadrotor UAV

In view of the fact that the output is only disturbed by error in most of the current system studies, this article proposes a closed-loop variable system model with error (both input and output signals are disturbed by noise) and designs the controller of the system. In this study, minimum variance controller and self-correcting minimum variance controller are designed using minimum variance control. Then an example is given to evaluate the performance of the designed controller using minimum variance performance evaluation. Finally, the closed-loop variable error system is combined with the quadrotor UAV (unmanned aerial vehicle), and the position controller in the position control loop of the quadrotor UAV is designed. The experimental results show that the controller has good performance and can well meet the design needs.

Fault Tolerant Super Twisting Sliding Mode Control of a Quadrotor UAV Using Control Allocation

In this study, a fault-tolerant super-twisting sliding mode controller with a control allocation system for a quadrotor aircraft is proposed. Super twisting sliding mode control is a robust control technique that handles a system with a relative degree equal to one. A super-twisting sliding mode controller is proposed because of its robustness to uncertainties and perturbations. It increases accuracy and reduces chattering. A control allocation algorithm is developed to cope with the actuator fault. Firstly, a nonlinear model of the quadrotor unmanned aerial vehicle (UAV) is presented. Then, the controller design and type of the actuator fault are explained. The control allocation algorithm is used to optimize the trajectory tracking performance of the quadrotor in the presence of an actuator fault. A control allocation algorithm is an effective approach to implementing fault-tolerant control. When actuator faults are identified, they can be modeled as changes in the B matrix of constraints. Various simulations have been made for situations with and without actuator failure. In normal conditions, the quadrotor can accurately track altitude, roll, pitch and yaw references. In faulty conditions, the quadrotor can follow the references with a small error. Simulations prove the effectiveness of the control allocation algorithm, which stabilizes the quadrotor in case of an actuator fault. Overall, this paper presents a novel fault-tolerant controller design for quadrotor aircraft that effectively addresses actuator faults using a super-twisting sliding mode controller and control allocation algorithm.

Quadrotor Composite Learning Neural Control with Disturbance Observer against Aerodynamic Disturbances

In this paper, a neural controller with disturbance observer is presented, in order to control a quadrotor Unmanned Aerial Vehicle (UAV) subject to aerodynamic disturbances. As a novel feature, the controller is formulated in a way to be implemented directly to a second-order system, such as the one of the quadrotor. Feedforward neural networks are employed in the control system design to compensate for internal disturbances, while the external disturbances and approximation error of the neural network are estimated by a disturbance observer. Moreover, composite learning is used to improve the overall performance, by estimating the state variables in real-time and using the estimation error in the updating rules of both the controller and the disturbance observer. An accurate disturbance modeling for the quadrotor is given, which considers wind and attitude changes, in order to evaluate the effectiveness of the controller. The controller successfully fulfills the task of trajectory tracking in the presence of wind and measurement noises, proving itself to be robust.

Design Procedure of a Low-Cost System for Energy Replenishment in a Quadrotor UAV through a Battery Exchange Mechanism

This paper describes the design and construction of an energy replenishment service station for a quadrotor. The prototype includes a small number of actuators, making it a low-cost solution. The system consists of three batteries: two charged and one discharged (within the quadrotor). Once the quadrotor lands, the battery with the highest charge is selected, which is then exchanged for the discharged battery. In order to perform this action, position control is used, in which the desired value depends on the location of the sensor that detects the highest voltage. In addition, the system has a mechanical design that facilitates the coupling of the unmanned aerial vehicle (UAV) with the structure for battery exchange, ensuring that the discharged battery is always in the same position. Furthermore, the design of a mechanism to release and hold the battery placed in the quadrotor is presented, which works by means of voltage and force sensors that identify the instant that the battery is discharged and when the UAV has landed on the exchange platform, thus initiating the exchange process. Likewise, the criteria for selecting the elements used, acquiring and processing signals, and routines for changing batteries are detailed.